ArticleApplied soft computing2007
Genetic Programming Neural Networks: A Powerful Bioinformatics Tool for Human Genetics.
Article in Applied soft computing, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis.BioData mining · 2024Article
- Gene-gene interaction: the curse of dimensionality.Annals of translational medicine · 2019Review
- Robust genetic interaction analysis.Briefings in bioinformatics · 2019Review
- Evolutionary computation: the next major transition of artificial intelligence?BioData mining · 2017Article
- Statistical and Computational Methods for Genetic Diseases: An Overview.Computational and mathematical methods in medicine · 2015Review
- Binning somatic mutations based on biological knowledge for predicting survival: an application in renal cell carcinoma.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP.PloS one · 2014Article
- ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network.BioData mining · 2013Article
- A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.BioMed research international · 2013Article
- Challenges and opportunities in genome-wide environmental interaction (GWEI) studies.Human genetics · 2012Review
- Article
- Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases.Genetic epidemiology · 2011Article
- A Bayesian method for identifying genetic interactions.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2009Article
- Methods for optimizing statistical analyses in pharmacogenomics research.Expert review of clinical pharmacology · 2009Article
- Neural networks for genetic epidemiology: past, present, and future.BioData mining · 2008Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The identification of genes that influence the risk of common, complex disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. This challenge is partly due to the limitations of parametric statistical methods for detecting genetic effects that are dependent solely or partially on interactions. We have previously introduced a genetic programming neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of genetic and gene-environment combinations associated with disease risk. Previous empirical studies suggest GPNN has excellent power for identifying gene-gene and gene-environment interactions. The goal of this study was to compare the power of GPNN to stepwise logistic regression (SLR) and classification and regression trees (CART) for identifying gene-gene and gene-environment interactions. SLR and CART are standard methods of analysis for genetic association studies. Using simulated data, we show that GPNN has higher power to identify gene-gene and gene-environment interactions than SLR and CART. These results indicate that GPNN may be a useful pattern recognition approach for detecting gene-gene and gene-environment interactions in studies of human disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.